Partially Observed Markov Decision Processes: Filtering, Learning and Controlled Sensing

Covering formulation, algorithms and structural results and linking theory to real-world applications in controlled sensing (including social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision processes (POMDPs). It emphasizes structural results in stochastic dynamic programming, enabling graduate students and researchers in engineering, operations research, and economics to understand the underlying unifying themes without getting weighed down by mathematical technicalities. In light of major advances in machine learning over the past decade, this edition includes a new Part V on inverse reinforcement learning as well as a new chapter on non-parametric Bayesian inference (for Dirichlet processes and Gaussian processes), variational Bayes and conformal prediction.

Author: Vikram Krishnamurthy
Binding Type: Hardcover
Publisher: Cambridge University Press
Published: 06/05/2025
Pages: 651
Weight: 2.95lbs
Size: 10.00h x 7.00w x 1.38d
ISBN: 9781009449434
2nd Edition